Representation Unlearning: Forgetting through Information Compression

TL;DR

This paper introduces Representation Unlearning, a novel method that removes specific data influence from models by transforming their representations, leading to more reliable forgetting and improved efficiency compared to traditional parameter-based approaches.

Contribution

It proposes a new framework for unlearning directly in the representation space, using information bottlenecks and variational surrogates, applicable in both data-available and zero-shot scenarios.

Findings

Achieves more reliable forgetting than baselines.

Maintains better utility of the model after unlearning.

Demonstrates greater computational efficiency.

Abstract

Machine unlearning seeks to remove the influence of specific training data from a model, a need driven by privacy regulations and robustness concerns. Existing approaches typically modify model parameters, but such updates can be unstable, computationally costly, and limited by local approximations. We introduce Representation Unlearning, a framework that performs unlearning directly in the model's representation space. Instead of modifying model parameters, we learn a transformation over representations that imposes an information bottleneck: maximizing mutual information with retained data while suppressing information about data to be forgotten. We derive variational surrogates that make this objective tractable and show how they can be instantiated in two practical regimes: when both retain and forget data are available, and in a zero-shot setting where only forget data can be accessed. Experiments across several benchmarks demonstrate that Representation Unlearning achieves more reliable forgetting, better utility retention, and greater computational efficiency than parameter-centric baselines.